Merchants use various statistical methods and models in an attempt to maximize the effectiveness of offers sent to consumers. Certain of such methods and models attempt to predict the likelihood that a given consumer or a set of consumers will respond favorably to a particular offer based on various geographic and demographic characteristics of the consumer. Other models look at historical purchase or payment data of current customers to predict follow-on behavior such as the current customer's future purchases or payment behavior.
In general, the accuracy of any model is dependent on the amount, relevancy, and quality of the input data. A model that includes every piece of information about a person and all their actions could make very accurate predictions regarding the future behaviors of that person. However, the amount of computing resources, data, and time needed to create an all-inclusive model makes such a system cost prohibitive to build and to maintain. In contrast, “mass mailings” tend to have minimal up-front modeling costs because offers are sent out with little or no regard to the recipients. However, this approach can have success rates (number of purchases/number of offers sent) of less than 1%, and thus generally are not effective. Sending such a large volume of offers that are ultimately never acted upon can cost a significant amount of money, and produces minimal returns. In addition to the two extreme approaches described above, other methods attempt to achieve an economic balance between maximizing offer success rates and minimizing the complexity, and thus the cost, of the predictive models.
One approach is the use of “marketing databases.” In general, marketing databases store information about current and/or potential customers. Such information can include data regarding a customer's previous purchases, customer service interactions, promotions received by a customer, geographic data, and demographic data. As the database accumulates more information about current customers, merchants can build models with predictive features, including determining which consumers are more likely to respond to certain offers in a positive fashion. Another approach is collaborative filtering, where a merchant compares prior purchases of one consumer to the prior purchases of another consumers. When the one consumer revisits the merchant (for example, in person, on the World Wide Web, or by receiving a catalog) the merchant presents the one consumer with offers for products or services purchased by the other consumers who have purchase histories similar to those of the one customer. This approach requires that the merchant have some historical purchase information about the one consumer.
Merchants also track the behavior of web site visitors by assigning each user a unique identifier which can then be kept in the URL as the visitor visits other pages of the site or stored as a “cookie” file in the browser. By this means, merchants and advertisers can track the pages viewed and the products purchased of each user. Thus, the operator may target ads, offers, or content accordingly. This approach is also based on a consumer's previous interactions with the merchant.